Modern wireless data communications systems can utilize the benefits of multiple transmit and receive antennas to improve both the range and data communications bandwidth. In particular, Multiple Input Multiple Output (MIMO) systems can utilize the spatio-temporal properties of the RF channel to transmit parallel but independent streams of data, greatly increasing the amount of data that can be transferred from a transmitter to a receiver. MIMO systems are therefore seeing widespread use in high-speed wireless digital transmission systems.
MIMO systems take advantage of the fact that radio frequency (RF) environments, particularly indoor RF environments, may contain large numbers of reflective and diffractive metallic objects, the effect of which is to impose multiple propagation modes on the RF channel existing between a transmitter and a receiver. Each propagation mode may be separately excited with an independent, uncorrelated RF signal; at the receiver, the signals occupying each propagation mode may be distinguished from each other and the original sets of uncorrelated RF signals may be extracted. The number of propagation modes available to a MIMO system is determined by the number of antennas at the transmitter and the receiver. With a sufficient number of transmit antennas, therefore, a MIMO transmitter may split a single stream of digital data into independent parallel streams and modulate each transmit antenna separately to transmit each parallel stream on a different propagation mode. Given an adequate number of receive antennas, and knowledge of the channel propagation modes, a MIMO receiver may separate and decode the parallel streams of data and may combine them to regenerate the original stream of digital data. The capacity of a MIMO system is therefore a multiple of the capacity of a conventional Single Input Single Output (SISO) system operating over the same RF channel.
FIG. 1 depicts an abstract representation of an RF environment containing MIMO wireless transmitter 101 with multiple transmit antennas 103 transmitting signals 106 to MIMO wireless receiver 102 with multiple receive antennas 104. A number of metallic objects (scatterers 107) may reflect and scatter signals 106. The presence of these scatterers may render it possible to separate and decorrelate the signal paths established between each pair of transmit and receive antennas. This in turn may cause the overall RF channel between transmit antennas 103 and receive antennas 104 to exhibit multiple channel propagation modes. (In FIG. 1, each propagation mode is depicted simplistically as a separate path between individual antenna pairs.) As previously noted, individual propagation modes may be used to transmit different portions of a digital data stream and increase the capacity of the system.
A MIMO channel in a RF wireless communication system with N transmit and M receive antennas may be represented as an N×M matrix of complex coefficients. Each coefficient represents the transfer function between an individual pair of transmit and receive antennas. A MIMO system cannot exploit more channel propagation modes than the lesser of the number of transmit and receive antennas. It may therefore be usual to set N=M so that the number of transmit and receive antennas is equal, and also equal to the number of channel propagation modes. In this case, the maximum number of parallel data streams that may be transmitted is also equal to N. Any receive or transmit antennas provided beyond this number may be used for improving the signal to noise ratio, but cannot be used to increase the system data bandwidth.
With reference to FIG. 2, an exemplary MIMO RF wireless communication system is depicted in simplified form. Such a system may comprise transmitter 101 and receiver 102. Transmitter 101 may accept a stream of digital transmit data 121 and process it using digital modulator 122, which may transform blocks of digital data to complex-valued digital modulation signals. These modulation signals may then be fed to transmit MIMO encoder 123, which may perform the space-time processing necessary to split up the transmit data stream into N individual component streams that may be converted to the analog domain by digital-to-analog (D/A) converters 124 and subsequently may be transformed to RF signals by RF up-converters 125, before being transmitted on at least N transmit antennas 126.
The signals generated by MIMO transmitter 101 may be transmitted over the RF channel 127, with the channel transfer function matrix being represented as [H]. Matrix [H] may have at least as many rows as there are transmit antennas 126 and further may have at least as many columns as there are receive antennas 129, and each element of [H] may represent the complex transfer function between an individual pair of transmit and receive antennas.
MIMO receiver 102 may utilize at least N receive antennas 129 to receive N different copies of the signals transmitted over the RF channel 127 and may pass them to RF down-converters 130, which may convert them to baseband for digitization by analog-to-digital (A/D) converters 131. The digitized signals may then be processed by receive MIMO equalization network 132, which may remove the effects of the channel matrix [H] to recover the original space-time coded signal transmitted on antennas 126. The equalized signal may then be passed to receive MIMO decoder 133 for space-time decoding to regain the modulated signals, which may then be further processed by receive demodulator 134 to produce receive data 135, which may be a regenerated version of the original transmit data 121.
It is apparent that MIMO equalizer 132 may play a key role in reception by compensating for the effects of RF channel 127 and extracting individual signal streams impressed into the various propagation modes of the channel matrix [H]. The equalization coefficients utilized by MIMO equalizer 132 may be entirely dependent on the channel matrix [H], which may have to be estimated at the receiver in order to receive the data. Estimation may be accomplished by preamble/pilot extractor 136, which may extract special training signals from the transmitted signal and pass them to MIMO channel estimator 137, which may use them to derive the equalization coefficients for MIMO equalizer 132.
It is understood that FIG. 2 is a simplified and abstract representation of a MIMO wireless transmitter and receiver, focusing only on aspects related to MIMO and channel estimation. Unrelated functions such as Medium Access Control (MAC) processing are omitted. It should further be understood that functions enhancing signal-to-noise ratios such as transmit precoding and beamforming, as well as receive diversity, are omitted for clarity.
Accurate estimates of the properties of the MIMO wireless channel that may exist between the receiver and transmitter may be used to determine the channel propagation modes, which may be used in turn to calculate the coefficients of the equalization matrices used by MIMO equalizer 132. This may aid in correctly decoding and separating the parallel data streams. A poor channel estimate may make it difficult or impossible to correctly separate the data streams, leading to a much higher error rate for the recovered data streams at the same signal level. Therefore, accurate channel estimation may be extremely important for MIMO systems.
The MIMO reception function may first determine the value of the channel matrix H using the known symbols in the preamble and pilot signals transmitted by MIMO transmitter 101; calculate an equalization matrix of complex coefficients corresponding to the H matrix; and may finally separate and equalize multiple streams of transmitted data by multiplying the received signal with the equalization matrix. (It should be understood that channel estimation may simply comprise the calculation of the equalization matrix, omitting the calculation of the intermediate [H] matrix.) Different methods of channel estimation are known: the simplest is the zero-forcing method, which consists of setting the equalization matrix to the inverse of the channel ([H]) matrix, while more complex methods may incorporate the noise statistics of the RF channel into the calculations. In all cases, however, it may be necessary to calculate the channel matrix H before data can be recovered.
In order to calculate the complex channel matrix H, a sequence of fixed, well-known and properly selected data may transmitted by MIMO transmitter 101 to MIMO receiver 102. MIMO receiver 102 may then calculate the channel matrix H that must be present for the original transmit data to have resulted in the data actually received, and may further convert this to an equalization matrix by various algorithms (e.g., zero-forcing, MMSE, sphere decoding). Due to distortion, transmit inaccuracy, and noise, it may not be possible to exactly calculate the channel matrix [H]; only estimates thereof may be obtained. Inaccuracies in the estimates may cause errors in the equalization matrix parameters, which in turn may increase the probability of errors in the decoded receive data. It therefore may be essential to make the channel estimates as accurate as possible in the face of noise and distortion, to maximize the probability of recovering the transmitted data.
In a packetized or framed wireless transmission protocol, examples of which may comprise IEEE 802.11 WLANs or 3GPP Long Term Evolution (LTE) protocols, channel estimation may initially be performed using special transmit data values referred to as training sequences. These special data values may be transmitted within the frame preamble at the start of a packet or frame. In the exemplary instance of IEEE 802.11, the data values transmitted may be referred to as the short and long training sequences respectively. Training sequences may support multiple functions, including but not limited to estimating the clock frequency and phase used by the transmitter, calculating the base received signal strength indication (RSSI) so that receiver RF gains may be configured, and estimating RF channel 127 interposed between MIMO transmitter 101 and MIMO receiver 102. MIMO RF channel 127 may be accurately estimated by measuring the baseband modulated representation of the actual training sequences received by MIMO receiver 102 and comparing it to the known value of the training sequences originally transmitted by MIMO transmitter 101. Once the MIMO channel has been estimated using these known signal values by channel estimator 137 and MIMO equalization network 132 has been loaded with the corresponding equalization matrix coefficients, MIMO transmitter 101 may send user data through MIMO channel 127. MIMO receiver 102 may use its MIMO equalizer to separate the streams and recover the data.
A significant issue that may be encountered in practice is that noise in MIMO RF channel 127, as well as signal noise within the circuits of MIMO transmitter 101 and MIMO receiver 102, may cause the coefficients of the equalization matrix calculated by channel estimator 137 to vary randomly from the actual (i.e., theoretically ideal) values. These variations may particularly impact the higher-order modulations (e.g., 64 and 256 Quadrature Amplitude Modulation, or QAM) and also larger frames, as these require very high signal-to-noise ratios and thus may be more susceptible to inaccuracies in the estimated channel. It may be observed that noise particularly affects the coefficients of the equalization matrix that have lower absolute values relative to the other coefficients; in the physical sense, these coefficients correspond to propagation paths between antenna pairs which have high path loss. The presence of noise in these coefficients may decrease the signal to noise ratio (SNR) and further may increase the correlation between propagation modes in MIMO channel 127, and may reduce the probability of error-free MIMO decoding.
Another significant issue that may occur is that, over time, the channel estimate may become inaccurate due to changing propagation conditions. For example, if MIMO transmitter 101 and MIMO receiver 102 move relative to each other, or if the positions of scatterers 107 changes, the statistical properties of the MIMO channel 127 (more specifically, the coefficients of the channel matrix [H]) may differ significantly from the initial estimate. This may induce errors in the decoded receive data stream, as the equalization matrix coefficients may no longer be accurate.
Yet another issue may be caused by the fact that channel estimation is begun afresh, without regard to previously calculated estimates, every time a frame is received. As a result, the data available from previous channel estimation calculations is forgotten and not used to improve channel estimation for successive frames.
To refine the channel estimate over time, pilot symbols may be introduced into the data stream. These may be known signal combinations that are periodically inserted into the transmitted RF by MIMO transmitter 101. As the pilot symbol values are known in advance, MIMO receiver 102 may use the pilot data to recalculate the RF channel matrix [H], and may thereby periodically refine the channel estimate as well as the equalization matrix.
An issue with this approach, however, may be that the number of pilot symbols is small relative to the number of data symbols. This may be done to maximize the transmission efficiency and data throughput, as the incorporation of pilot symbols may proportionately reduce the capacity available for data. Another issue may be that the pilot symbols are much shorter and sparser than the training sequences transmitted in the preamble; therefore, the accuracy of channel estimation performed using the pilot symbols may be much less than that available from the training sequences. In fact, it may be observed in practice that the presence of noise and distortion may actually result in increased errors when using the channel estimates from the pilot symbols, as compared to not using them at all. Channel estimation may therefore take the sparsity of the pilot symbols into account when refining the channel estimate over time.
Digital wireless systems employ higher channel bandwidths in order to transfer increased amounts of data. With increased bandwidths, however, the RF channel may not be regarded as frequency-flat; i.e., the channel frequency response may not be uniform across the entire transmission bandwidth and selective fading may occur. Further, variation in propagation delays of the different paths through the transmission medium, for example paths 106 in FIG. 1, may become large relative to the interval between individual modulation symbols. This may result in inter-symbol interference.
Orthogonal Frequency Division Multiplexing (OFDM) modulation may be used to address these issues in high-bandwidth digital wireless data systems. An OFDM system subdivides the available channel bandwidth into small segments (referred to as subcarriers), and may transmit different blocks of data over each subcarrier separately. As the frequency range occupied by individual subcarriers is small, the frequency response for each individual segment or subcarrier can be regarded as flat. Further, as multiple symbols may be transmitted on the different subcarriers within one symbol period, it may be possible to substantially increase the symbol period while still transmitting the same amount of data, which will eliminate problems due to inter-symbol interference and symbol smearing.
FIG. 3 shows a simplified representation of a single OFDM frame, where horizontal axis 157 represents time and vertical axis 158 represents frequency. As depicted, an OFDM frame is a 2-dimensional structure that may comprise comprising m symbols each of fixed time duration, with each symbol comprising n subcarriers each of fixed frequency width. For example, the IEEE 802.11 symbol duration is either 4.0 or 3.6 microseconds and 52 subcarriers are used with frequency widths ranging from 78.125 kHz to 312.5 kHz. The OFDM frame may comprise a preamble 150, which may further consist of short training sequence 152, long training sequence 154, and other preamble data 154. After preamble 150, the OFDM frame may contain one or more symbols 151 of payload information. The payload may be divided up into data 155 and pilot signals 156. In the case of IEEE 802.11, there may be 48 data subcarriers and 4 pilot subcarriers.
OFDM modulation may be beneficially used in conjunction with MIMO systems, as the increased symbol duration relative to the transmission bandwidth may enable the MIMO system to handle significantly larger delay spreads (i.e., time delay differences between the shortest and longest viable RF paths). In this case, multiple parallel streams of OFDM modulated data can be transmitted within each individual MIMO channel propagation mode. This is referred to as an OFDM MIMO system.
An example of an OFDM MIMO communication system is represented in FIG. 4. It may comprise OFDM MIMO transmitter 170 that may accept digital transmit data 121, encodes it using binary convolutional code (BCC) coder 171, and partitions the data to be mapped on to multiple MIMO streams using MIMO stream mapper 172. The individual streams of data may then be modulated on to subcarriers using OFDM modulators 173, after which MIMO space-time encoding may be performed using MIMO space-time encoder 174. The frequency-domain representation of the modulated subcarriers may then be converted to time-domain symbols and waveshaped using inverse Fast Fourier Transform (IFFT) and symbol shaper blocks 175. The shaped symbols may be converted to analog using D/A converters 124 and upconverted to RF signals using RF upconverters 125, before transmission on multiple antennas 126.
The MIMO OFDM signals generated from antennas 126 may then propagate through the RF channel (not shown for simplicity) and reach antennas 129 of MIMO OFDM receiver 181. These signals may be converted to baseband and digitized using RF downconverters 130 and A/D converters 131, after which the signal received from each antenna may be converted to the frequency domain using Fast Fourier Transform (FFT) blocks 176. The individual signal streams may be equalized using MIMO equalizer 177, which may remove the effects due to the space-time encoding and intervening MIMO transmission channel and restore the original space-time streams. The subcarriers comprising each space-time stream may then be demodulated using OFDM demodulators 178 and passed to MIMO stream demapper 179, which may transform the multiple space-time streams to a single data stream. Decoding of the encoded transmit data may be performed using Viterbi decoder 180, which may also correct bit errors that may be present in the received data due to noise and poor channel estimates and output the resulting receive data 135.
Synchronization and channel estimation may be performed by extracting preamble and pilot information from the received baseband signal with preamble/pilot extractor 136, which may supply these special signals to clock synchronization logic 182 (to align receive and transmit clocks) and to MIMO channel estimator 137. MIMO channel estimator 137 may produce an estimate of the channel matrix [H], as well as calculating the equalization matrix parameters that may be passed to MIMO equalizer 177. This calculation may be similar to that depicted in FIG. 5, wherein equalization [U] matrix 215 is calculated within MIMO receiver 214 from preamble and pilot information received by antennas 213 after being transmitted by MIMO transmitter 210 over antennas 211 and traversing MIMO RF channel 212. The various terms in the equalization matrix (e.g., U11, U22, etc.) may approximately correspond to different “branches” of the MIMO RF channel. The overall equalization [U] matrix may be regarded, in a simplified view, as a matrix which is to be multiplied into the signals received from the receive antennas in order to reverse the effect of the space-time encoder and MIMO RF channel upon the originally transmitted signal streams, and thereby recover the streams.
In a standard MIMO receiver, channel estimation may be performed as a single operation over the entire RF channel, as it is assumed that the channel is uniform across its width. In an OFDM MIMO case, however, this assumption may not hold true. OFDM MIMO receiver 181 may hence perform channel estimation separately for each individual subcarrier, and may also compute an equalization matrix separately for each subcarrier. For example, in the case of IEEE 802.11 WLANs, MIMO channel estimator 137 may calculate 48 different channel estimation matrices [H] (i.e., one for each data subcarrier) and supply 48 different equalization matrices to MIMO equalizer 177. MIMO equalizer 177 may then process each FFT bin using a separate respective equalization matrix corresponding to each subcarrier. In this situation, the training sequences in the frame preamble may be specially constructed to facilitate this, by using orthogonal signal sequences for each subcarrier within the preamble to improve the accuracy of channel estimation.
A significant issue that may be encountered in practice with wireless communication systems such as IEEE 802.11 systems is maintaining accuracy of channel estimation over very long frames. For example, IEEE 802.11 systems support aggregated Medium Access Control (MAC) Protocol Data Units (PDUs) (i.e., A-MPDUs) that comprise multiple MAC frames concatenated together with a single preamble at the start. Such A-MPDUs may become very long, containing hundreds of thousands of bytes of information. However, as an A-MPDU may only contain a single preamble, there may only be one initial training sequence that may be used for channel estimation. Further, the pilot symbols may not be adequate for improving the channel estimate adequately to eliminate the bit errors. As a result, the channel estimate calculated at the beginning of the A-MDPU may degrade substantially towards the end of the A-MPDU, leading to bit errors in the received data.
As the bandwidth needs of digital wireless systems further increase relative to the available RF spectrum, Multi-user MIMO (MU-MIMO) technology is being introduced to enhance the capacity of OFDM MIMO systems. MU-MIMO may involve multiple OFDM MIMO transmitters that simultaneously transmit different data streams to the same receiver using the same RF channel at the same time, or may involve multiple OFDM MIMO receivers that simultaneously receive different transmissions from a single OFDM MIMO transmitter over the same RF channel. MU-MIMO exploits the fact that different channel propagation modes exist between every MIMO transmitter and receiver pair. Normally, the transmission of multiple signals within the same RF channel bandwidth at the same time may result in mutual interference between the signals, and a receiver may not be able to distinguish one signal from another. However, if the channel matrix existing between every transmitter/receiver pair is accurately known, uncorrelated propagation modes may be selected for transporting data between every different transmitter/receiver pair. In this case, the receiver(s) may use this information to maximize the signal-to-noise ratio (SNR) of the desired signal and minimize the SNR of the undesired (interfering) signals. A step of successive interference cancellation may then be employed to remove all of the undesired signals and extract the data from the desired signal, as the propagation modes are uncorrelated.
As represented in FIG. 6, MU-MIMO receiver 200 may receive signals 204 simultaneously from MU-MIMO transmitters 201, 202, 203. The presence of many scatterers 205 in the RF propagation environment may cause the propagation modes existing between MU-MIMO receiver 200 and each of MU-MIMO transmitters 201, 202, 203 to be different. By exciting these different propagation modes with distinct signals at each of the transmitters, and decorrelating and extracting them at the receiver, it may be possible to simultaneously transmit information from each of the transmitters 201, 202, 203 to receiver 200 over the same frequency band. It may be understood that the scenario depicted in FIG. 6, namely multiple transmitters communicating to a single receiver, is also applicable in the reverse direction—i.e., multiple receivers receiving signals from a single transmitter.
The implementation of MU-MIMO, however, may demand very accurate knowledge of the RF channel existing between each transmitter/receiver pair. Without a sufficiently accurate channel estimate, it may not be possible to identify the propagation modes used for data transmission between each pair, and thus to separate the different streams of data. The training sequences within the preamble may be relatively short (e.g., 16 microseconds for IEEE 802.11) and may not provide sufficient data for an accurate channel estimate. Further, the impact of Doppler shifts due to relative motion of different transmitter/receiver pairs, or channel changes due to changes in the RF environment, are greatly increased. Therefore, MU-MIMO systems may require the initial channel estimate and corresponding equalization matrices to be calculated more accurately, and may further demand that these values be constantly updated over time. Otherwise, as the computed channel estimates deviate further away from the actual RF channel over time, the error rate will increase, and eventually the signal may not be received. This may be a particular problem for long frames, such as frame bursts. In this case, the RF channel calculated at the start of the frame may be quite different from the RF channel that exists at the end of the frame or burst, resulting in the frames at the end of the burst being lost. However, as mentioned previously, the pilots may be too sparse to provide accurate channel estimates, and a better means of updating the channel estimates over time may be required to support MU-MIMO.
It may be apparent from the foregoing discussion that channel estimation is important for efficient high-bandwidth wireless communication. It may further be apparent that more advanced technologies such as OFDM MIMO and MU-MIMO may greatly increase the need for accurate and reliable channel estimation and equalization. However, the initial channel estimates that may be obtained from the training sequences embedded in the preamble may not be adequate for long OFDM-MIMO frames due to changes in the statistical properties of the RF channel over time. In addition, the substantially increased need for accurate channel estimation and equalization in MU-MIMO may not be addressed by relatively short training sequences, and the drift in the channel properties may not be adequately compensated for by the pilot signals.
There is hence a need for improved MIMO wireless channel estimation and equalization systems and methods. A system that can increase the number of symbols used for channel estimation beyond those allocated in the frame preamble may be desirable, for example by utilizing the symbols transmitted in the frame payload. A system providing improved channel estimation for use with long received frames, such as IEEE 802.11 A-MPDUs, may be desirable. Further, a system that can maintain and refine a channel estimate for a given transmitter/receiver pair over time may be desirable. Such a system may preferably improve the quality of the initial channel estimate, eliminating channel noise from the estimate, and may preferably track changes in the MIMO RF channel statistical properties over time to permit larger frames to be handled. Finally, it may be desirable for such a system to be realized without significant additional hardware cost and complexity.